Methods and systems for training a crowdworker

ABSTRACT

Disclosed embodiments illustrate methods and systems implementable on a computing device for training a crowdworker on one or more skill sets. The crowdworker attempts a first set of tasks. The method includes determining an expertise gap between a current level of expertise possessed by the crowdworker in the one or more skill sets and a required level of expertise in the one or more skill sets. Further, the method includes determining a number of training tasks that the crowdworker has to complete to achieve the required level of expertise in the one or more skill sets. At least one training task pertaining to the one or more skill sets is assigned along with the first set of tasks such that the crowdworker gets trained on the one or more skill sets in a predetermined period without violating a service level agreement associated with the first set of tasks.

This patent application is a continuation of U.S. patent applicationSer. No. 13/890,280 filed on May 9, 2013, entitled METHODS AND SYSTEMSFOR TRAINING A CROWDWORKER.

TECHNICAL FIELD

The presently disclosed embodiments are related, in general, tocrowdsourcing. More particularly, the presently disclosed embodimentsare related to systems and methods for training a crowdworker.

BACKGROUND

Crowdsourcing platform allows distribution of one or more tasks to oneor more crowdworkers. The one or more crowdworkers may be looselydefined as groups of individual crowdworkers that may work on the one ormore tasks. The crowdsourcing platform may receive the one or more tasksfrom a requestor. The requestor may declare a service level agreementwith the crowdsourcing platform indicating the timelines and theremuneration details associated with the one or more tasks.

The crowdsourcing platform may distribute the one or more tasks as perthe skill set of the one or more crowdworkers. For example, the one ormore tasks include a first set of tasks that require a crowdworkerpossessing a first skill set. The crowdsourcing platform identifies aset of crowdworkers from the one or more crowdworkers that possess thefirst skill set. Thereafter, the crowdsourcing platform presents thefirst set of tasks to the identified set of crowdworkers. One or more ofthe identified set of crowdworkers may opt to attempt the presentedtasks.

In a scenario, when the bandwidth of the identified set of crowdworkersis full, and the crowdsourcing platform receives more tasks that requirethe set of crowdworkers possessing the first skill set, thecrowdsourcing platform may not be able to distribute the tasks(requiring first skill set) to the identified set of crowdworkers.Therefore, the crowdsourcing platforms may violate the SLA withrequestor and thereby the business may get hampered.

Thus, there is a need to train other crowdworkers on a desired sills(e.g., the first skill set) such that the crowdsourcing platform is ableto counter such scenarios.

SUMMARY

According to embodiments illustrated herein there is provided a methodimplementable on a computing device for training a crowdworker on one ormore skill sets. The crowdworker attempts a first set of tasks. Themethod includes determining an expertise gap between a current level ofexpertise possessed by the crowdworker in the one or more skill sets anda required level of expertise in the one or more skill sets. Further,the method includes determining a number of training tasks that thecrowdworker has to complete to achieve the required level of expertisein the one or more skill sets. At least one training task pertaining tothe one or more skill sets is assigned along with the first set of taskssuch that the crowdworker gets trained on the one or more skill sets ina predetermined period without violating a service level agreementassociated with the first set of tasks. The processor in the computingdevice performs the method.

According to embodiment illustrated herein a crowdsourcing platformserver for training a crowdworker on one or more skill sets. Thecrowdworker attempts a first set of tasks. The crowdsourcing platformserver includes a processor. Further, the crowdsourcing platform servercomprises a memory that further comprises an expertise determinationmodule configured to determine an expertise gap between a current levelof expertise possessed by the crowdworker in the one or more skill setsand a required level of expertise in the one or more skill sets.Further, the expertise determination module is configured to determine anumber of training tasks that the crowdworker has to complete to achievethe required level of expertise in the one or more skill sets. Thememory further comprises a task manager configured to assign at leastone training task pertaining to the one or more skill sets along withthe first set of tasks such that the crowdworker gets trained on the oneor more skill sets in a predetermined period without violating a servicelevel agreement associated with the first set of tasks.

According to embodiments illustrated herein there is provided a computerprogram product for use with a computer. The computer program productcomprising a non-transitory computer readable medium embodied therein aset of instructions executable by a processor for training a crowdworkeron one or more skill sets. The crowdworker attempts a first set oftasks. The set of instructions includes a program instruction means fordetermining an expertise gap between a current level of expertisepossessed by the crowdworker in the one or more skill sets and arequired level of expertise in the one or more skill sets. Further, theset of instructions includes a program instruction means for determininga number of training tasks that the crowdworker has to complete toachieve the required level of expertise in the one or more skill sets.Additionally, the set of instructions includes a program instructionmeans for assigning at least one training task pertaining to the one ormore skill sets along with the first set of tasks such that thecrowdworker gets trained on the one or more skill sets in apredetermined period without violating a service level agreementassociated with the first set of tasks.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings illustrate various embodiments of systems,methods, and other aspects of the disclosure. Any person having ordinaryskill in the art will appreciate that the illustrated element boundaries(e.g., boxes, groups of boxes, or other shapes) in the figures representone example of the boundaries. It may be that in some examples, oneelement may be designed as multiple elements or that multiple elementsmay be designed as one element. In some examples, an element shown as aninternal component of one element may be implemented as an externalcomponent in another, and vice versa. Furthermore, elements may not bedrawn to scale.

Various embodiments will hereinafter be described in accordance with theappended drawings, which are provided to illustrate, and not to limit,the scope in any manner, wherein like designations denote similarelements, and in which:

FIG. 1 is a block diagram of a system environment in which variousembodiments can be implemented;

FIG. 2 is a block diagram of a crowdsourcing platform server, inaccordance with at least one embodiment;

FIG. 3 depicts a table illustrating an example representation of theskill set matrix data, in accordance with at least one embodiment;

FIG. 4 is a flowchart illustrating a method for assigning one or moretasks to one or more crowdworkers, in accordance with at least oneembodiment; and

FIG. 5A and FIG. 5B is a graphical representation illustrating alearning curve of a crowdworker, in accordance with at least oneembodiment.

DETAILED DESCRIPTION

The present disclosure is best understood with reference to the detailedfigures and description set forth herein. Various embodiments arediscussed below with reference to the figures. However, those skilled inthe art will readily appreciate that the detailed descriptions givenherein with respect to the figures are simply for explanatory purposesas the methods and systems may extend beyond the described embodiments.For example, the teachings presented and the needs of a particularapplication may yield multiple alternate and suitable approaches toimplement the functionality of any detail described herein. Therefore,any approach may extend beyond the particular implementation choices inthe following embodiments described and shown.

References to “one embodiment”, “at least one embodiment”, “anembodiment”, “one example”, “an example”, “for example” and so on,indicate that the embodiment(s) or example(s) so described may include aparticular feature, structure, characteristic, property, element, orlimitation, but that not every embodiment or example necessarilyincludes that particular feature, structure, characteristic, property,element or limitation. Furthermore, repeated use of the phrase “in anembodiment” does not necessarily refer to the same embodiment.

Definitions: The following terms shall have, for the purposes of thisapplication, the respective meanings set forth below.

A “task” refers to a piece of work, an activity, an action, a job, aninstruction or an assignment to be performed. Tasks may necessitate theinvolvement of one or more crowdworkers. Examples of tasks include, butare not limited to, digitization of a document, generating a report,evaluating a document, conducting a survey, writing a code, extractingdata, translating text, and the like.

“Crowdsourcing” refers to distributing tasks by soliciting theparticipation of loosely defined groups of individual crowdworkers. Agroup of crowdworkers may include, for example, individuals respondingto a solicitation posted on a certain website such as, but is notlimited to, Amazon Mechanical Turk and Crowd Flower.

A “crowdsourcing platform” refers to a business application, wherein abroad, loosely defined external group of people, communities, ororganizations provides solutions as outputs for any specific businessprocesses received by the application as input. In an embodiment, thebusiness application may be hosted online on a web portal (e.g., thecrowdsourcing platform servers). Various examples of the crowdsourcingplatforms include, but are not limited to, Amazon Mechanical Turk orCrowd Flower.

A “crowdworker” refers to a workforce/worker(s) that may perform one ormore tasks, which generate data that contributes to a defined resultsuch as proofreading a part of a digital version of an ancient text oranalyzing a quantum of a large volume of data. According to the presentdisclosure, the crowdworker(s) includes, but is not limited to, asatellite center employee, a rural business process outsourcing (BPO)firm employee, a home-based employee, or an internet-based employee.Hereinafter, “crowdworker”, “worker”, “remote worker” “crowdsourcedworkforce”, “crowdworker”, and “crowd” may be interchangeably used.

A “skill set” refers to a set of skills possessed by a crowdworker. Inan embodiment, the crowdworker may utilize the set of skills to completethe task. Some examples of the skill set may include, but are notlimited to, digitizing electronic document, image labeling, translation,etc.

A “service level agreement (SLA)” refers to an agreement between acrowdsourcing platform and a crowdworker. In an embodiment, the SLA maydefine a time interval in which the crowdworker is bound to complete atask, a required accuracy, and remuneration associated with completionof the task.

A “level of expertise” refers to a measure of expertise of a crowdworkerin a particular skill set. In an embodiment, the level of expertise mayinclude, but not limited to, an expert level, an advanced level, and abasic level. In an embodiment, if a first crowdworker possesses theexpert level in the particular skill set, the crowdworker will be ableto complete all the tasks pertaining to the particular skill set instipulated period accurately (e.g., adhere to SLAs). Similarly, if asecond crowdworker possesses the advanced level in the particular skillset, the crowdworker will be able to complete most of the taskspertaining to the particular skill set with acceptable accuracy.Further, the second crowdworker may violate SLA associated with some ofthe tasks pertaining to the particular skill set. If a third crowdworkerpossesses the basic level of expertise in the skill set, the crowdworkerwill be able to complete very few tasks pertaining to the particularskill set in comparison to the first crowdworker and the secondcrowdworker.

FIG. 1 is a block diagram of a system environment 100 in which variousembodiments can be implemented. The system environment 100 includes acrowdsourcing platform server 102, a database server 104, a network 106,and a computing device 108.

The crowdsourcing platform server 102 is configured to host one or morecrowdsourcing platforms. One or more crowdworkers are registered with acrowdsourcing platform. Further, the crowdsourcing platform offers oneor more tasks to the one or more crowdworkers. In an embodiment, thecrowdsourcing platform offers the one or more tasks based on the one ormore skill sets possessed by each of the one or more crowdworkers.Further, the crowdsourcing platform trains the one or more crowdworkersby offering one or more training tasks along with the one or more tasks.In an embodiment, the crowdsourcing platform presents a user interface(UI) to the one or more crowdworkers through a web based interface or aclient application. The one or more crowdworkers may access the one ormore tasks through the web based interface or the client application.Further, the one or more crowdworkers may submit a final workproduct/response to the crowdsourcing platform through the UI. Thecrowdsourcing platform may validate the response for quality checks. Inan embodiment, the crowdsourcing platform server 102 may be realizedthrough an application server such as but not limited to, Javaapplication server, .NET framework, and Base4 application server. Thecrowdsourcing platform server 102 is described later in conjunction withFIG. 2.

The database server 104 stores information associated with the one ormore crowdworkers. In an embodiment, the information associated with theone or more crowdworkers may include, but not limited to, a skill setmatrix associated with each of the one or more crowdworkers, avalidation data associated with previously completed tasks. Further, thedatabase server 104 stores metadata associated with the one or moretasks. The metadata associated with the one or more tasks is describedlater in conjunction with FIG. 4. The database server 104 may berealized through various technologies, such as, but not limited to,Microsoft® SQL server, Oracle, and My SQL. In an embodiment, thecrowdsourcing platform server 102 and/or the computing device 108 mayconnect to the database server 104 using one or more protocols such as,but not limited to, ODBC protocol and JDBC protocol.

A person having ordinary skills in the art would understand that thescope of the disclosure is not limited to the database server 104 as aseparate entity. In an embodiment, the functionalities of the databaseserver 104 can be integrated into the crowdsourcing platform server 102.

The network 106 corresponds to a medium through which content andmessages flow between various devices of the system environment 100(e.g., the crowdsourcing platform server 102, the database server 104,and the computing device 108). Examples of the network 106 may include,but are not limited to, a Wireless Fidelity (Wi-Fi) network, a Wide AreaNetwork (WAN), a Local Area Network (LAN), or a Metropolitan AreaNetwork (MAN). Various devices in the system environment 100 can connectto the network 106 in accordance with the various wired and wirelesscommunication protocols such as Transmission Control Protocol andInternet Protocol (TCP/IP), User Datagram Protocol (UDP), and 2G, 3G, or4G communication protocols.

The computing device 108 presents the UI (received from thecrowdsourcing platform) to a crowdworker. In an embodiment, the UI is aweb interface facilitated by the crowdsourcing platform. The crowdworkerreceives a task from the crowdsourcing platform through the UI. Further,the crowdworker submits the final work product/response through the UIon the computing device 108. Some of the examples of the computingdevice 108 include a personal computer, a laptop, a PDA, a mobiledevice, a tablet, or any device that has the capability to display theuser interface to the crowdworker.

FIG. 2 is a block diagram of the crowdsourcing platform server 102, inaccordance with at least one embodiment. The crowdsourcing platformserver 102 includes a processor 202, a transceiver 204, and a memory206. The crowdsourcing platform server 102 is described in conjunctionwith FIG. 1.

The processor 202 is coupled to the transceiver 204 and the memory 206.The processor 202 executes a set of instructions stored in the memory206 to perform a predetermined operation on the crowdsourcing platformserver 102. The processor 202 can be realized through a number ofprocessor technologies known in the art. Examples of the processor 202may include, but are not limited to, microprocessor such as X86processor, RISC processor, ASIC processor, CISC processor, ARMprocessor, or any other processor.

The transceiver 204 transmits and receives messages and data to/fromvarious components of the system environment 100 (e.g., the computingdevice 108 and the database server 104) through the network 106.Examples of the transceiver 204 may include, but are not limited to, anantenna, an Ethernet port, a USB port or any other port that can beconfigured to receive and transmit data. The transceiver 204 transmitsand receives data/messages in accordance with the various communicationprotocols, such as, TCP/IP, UDP, and 2G, 3G, or 4G communicationprotocols.

The memory 206 stores a set of instructions and data. Some of thecommonly known memory implementations include, but are not limited to, arandom access memory (RAM), a read only memory (ROM), a hard disk drive(HDD), and a secure digital (SD) card. Further, the memory 206 includesa program module 208 and a program data 210. The program module 208includes a set of instructions that is executable by the processor 202to perform specific operations. The program module 208 further includesa communication manager 212, an expertise determination module 214, atime interval determination module 216, a task manager 218, a validationmodule 220, an SLA manager 222, and a learning curve predictor 224. Itis apparent to a person having ordinary skills in the art that the setof instructions stored in the memory 206 enables the hardware of thecrowdsourcing platform server 102 to perform the predeterminedoperation.

The program data 210 includes an allocation data 226, a skill set matrixdata 228, a time interval data 230, a task data 232, a crowdworker data234, a historical task data 236, and a validation data 238.

The communication manager 212 receives one or more tasks from arequestor through the transceiver 204. The communication manager 212stores the one or more tasks as the task data 232. In an embodiment, thecommunication manager 212 further stores metadata associated with theone or more tasks as the task data 232. Further, the communicationmanager 212 transmits the UI to the computing device 108 through thetransceiver 204. The communication manager 212 receives responses forthe one or more tasks from the one or more crowdworkers through the UI.The communication manager 212 includes various protocol stacks such as,but not limited to, TCP/IP, UDP, and 2G, 3G, or 4G communicationprotocols. The communication manager 212 transmits and receives themessages/data (e.g., images) through the transceiver 204 in accordancewith such protocol stacks.

The expertise determination module 214 extracts information associatedwith one or more crowdworkers from the crowdworker data 234. In anembodiment, the information associated with the one or more crowdworkersinclude, but are not limited to, credentials associated with each of theone or more crowdworkers, a set of tasks previously completed by each ofthe one or more crowdworkers, and an accuracy score on each task in theset of tasks, a first time interval consumed by the crowdworker tocomplete the set of tasks. The expertise determination module 214utilizes the information associated with the one or more crowdworkers todetermine one or more skill sets of the one or more crowdworkers.Additionally, the expertise determination module 214 determines acurrent level of expertise of the one or more crowdworkers in each ofthe one or more skill sets based on the accuracy score and the firsttime interval. Further, the expertise determination module 214determines a required level of expertise that the one or morecrowdworker needs to achieve from the metadata associated with the oneor more tasks. Based on the required level of expertise and the currentlevel of expertise, the expertise determination module 214 determines anexpertise gap. The expertise determination module 214 stores the currentlevel of expertise, the required level of expertise, and the expertisegap as the skill set matrix data 228. The skill set matrix data 228 isdescribed later in conjunction with FIG. 3. The determination ofexpertise gap is described later in conjunction with FIG. 4. In anembodiment, the expertise determination module 214 utilizes one or moredata mining techniques such as, but is not limited to, anomalydetection, clustering, classification, regression, and summarization.

The time interval determination module 216 determines the expertise gapfor each of the one or more crowdworkers in each of the one or moreskill sets from the skill set matrix data 228. Based on the expertisegap, the time interval determination module 216 determines a second timeinterval. In an embodiment, the second time interval is indicative of atime that a crowdworker from the one or more crowdworkers will take tocomplete a task of a particular skill set. The time intervaldetermination module 216 stores the second time interval as the timeinterval data 230.

The task manager 218 extracts the metadata of the one or more tasks fromthe task data 232. In an embodiment, the metadata associated with theone or more tasks includes, but are not limited to, a third timeinterval in which each of the one or more tasks has to be completed, anda skill set required for completing each of the one or more tasks. Basedon the metadata, the task manager 218 determines a skill set requiredfor each of the one or more tasks. The task manager 218 determinesrespective skill sets of each of the one or more crowdworkers from theskill set matrix data 228. Further, the task manager 218 determines thesecond time interval associated with each of the one or morecrowdworkers for completing a task pertaining to the respective skillsets. Thereafter, the task manager 218 offers the one or more tasks tothe one or more crowdworkers based on the second time interval, therespective skill sets, and the metadata associated with the one or moretasks. In an embodiment, the task manager 218 offers at least onetraining task to the one or more crowdworkers pertaining to at least oneof the respective skill sets or a skill set other than the respectiveskill sets. The task assignment is described later in conjunction withFIG. 4.

The validation module 220 validates the responses for the one or moretasks and the training task. The validation module 220 checks whetherthe responses for the one or more tasks and the training task arecorrect. Further, the validation module 220 generates a validationresult based on the check. In an embodiment, the validation module 220utilizes at least one of a consensus technique to validate the responsesfor the training task and the one or more tasks. The validation module220 stores the validation result as the validation data 238. In anembodiment, the validation module 220 may be realized through acomparator that compares the responses for the one or more tasks withthe one or more correct responses to generate the validation data 238.Based the validation data 238, the expertise determination module 214determines an expertise gain of each of the one or more crowdworkers inat least one of the respective skill sets or the skill set other thanthe respective skill sets.

The SLA manager 222 extracts the validation result from the validationdata 238. Further, the SLA manager 222 determines an actual timeconsumed by the one or more crowdworkers to submit the response for eachof the one or more tasks. Based on the validation result and the timeconsumed, the SLA manager 222 determines a number of instances for eachof the one or more crowdworkers where the each of the one or morecrowdworkers has violated the SLA.

The learning curve predictor 224 predicts a learning curve for each ofthe one or more crowdworkers. The learning curve predictor 224determines the expertise gain of each of the one or more crowdworkers.Based on the expertise gain, the learning curve predictor 224 predictsthe learning curve of each of the one or more crowdworkers in at leastone of the respective skill sets or the skill set other than therespective skill sets.

FIG. 3 depicts a table 300 illustrating an example representation of theskill set matrix data 228, in accordance with at least one embodiment.The table 300 is described in conjunction with FIG. 1 and FIG. 2.

The table 300 includes a column 302 illustrating a list of names ofcrowdworkers. For instance, the column 302 includes the name or ID of“crowdworker-1” (depicted by 314) and the name or ID of “crowdworker-2”(depicted by 324). Further, the table 300 includes a column 304illustrating a list of one or more skills associated with each of theone or more crowdworkers (listed in the column 302). For instance, the“crowdworker-1” (depicted by 314) possesses the “skill-1” (depicted by316). Columns 308, 310, and 312 together depict levels of expertise(depicted by 306). The column 308 illustrates whether a crowdworkerpossesses a basic level of expertise in the one or more skill sets. Forexample, the “crowdworker-1” (depicted by 314) possesses a basic levelof expertise (depicted by 318) in the “skill-1” (depicted by 316).Similarly, the “crowdworker-1” (depicted by 314) possesses an advancedlevel of expertise (depicted by 322) in the “skill-2” (depicted by 320).

FIG. 4 is a flowchart 400 illustrating a method for training one or morecrowdworkers, in accordance with at least one embodiment. The flowchart400 is described in conjunction with FIG. 1, FIG. 2, and FIG. 3.

At step 402, a current level of expertise of the one or morecrowdworkers is determined for each of the one or more skill sets. In anembodiment, the expertise determination module 214 determines thecurrent level of expertise of the one or more crowdworkers. Prior todetermining the current level of expertise, the communication manager212 receives the one or more tasks from a requestor. The communicationmanager 212 stores the one or more tasks as the task data 232. Further,the communication manager 212 stores the metadata associated with theone or more tasks as the task data 232. Following table illustrates anexample representation of the metadata:

TABLE 1 Illustrates metadata associated with the one or more tasksExpertise level Third time One or more tasks Required skill set requiredinterval Task-1 Skill-1 Basic  5 days Task-2 Skill-2 Expert 0.5 day Task-3 Skill-1 Advanced 1 day

For example, the “task-1” requires a crowdworker having the “basiclevel” of expertise in the “skill-1” to complete the task. Further, 5days may be required for completing the “task-1”. Similarly, for the“task-2”, a crowdworker having “expert” level of expertise in “skill-2”is required to complete the task. Further, 0.5 days may be required forcompleting the “task-2”.

Thereafter, the expertise determination module 214 determines thecurrent level expertise of the one or more crowdworkers in the one ormore skills set from the skill set matrix data 228. For example, theexpertise determination module 214 determines that a crowdworkerpossesses the “basic level” expertise in the “skill-1”. In anembodiment, the current level of expertise of the one or morecrowdworkers is predetermined.

At step 404, the expertise gap between the current level of expertiseand the required level of expertise is determined. In an embodiment, theexpertise determination module 214 determines the expertise gap. Priorto determining the expertise gap, the expertise determination module 214determines the required level of expertise from the metadata associatedwith the one or more tasks (refer Table 1). Further, the expertisedetermination module 214 determines the current level of expertise foreach of the one or more crowdworkers from the information associatedwith the each of the one or more crowdworkers (stored as the crowdworkerdata 234). Thereafter, the expertise determination module 214 digitizeseach of the current level of expertise and the required level ofexpertise using a step function. In an embodiment, following tableillustrates mapping between the various expertise levels and thecorresponding discrete levels:

TABLE 2 Illustrates mapping between the expertise level and thecorresponding discrete value range. Expertise level Discrete value rangeBasic level   0-0.33 Advanced level 0.33-0.66 Expert level 0.66-0.99

For example, for the “basic” level of expertise, the discrete valuerange is “0-0.33”. Similarly, for the “advanced” level of expertise andthe “expert” level of expertise, the discrete value range is “0.33-0.66”and “0.66-0.99,” respectively.

Post digitization, of the current level and the required level ofexpertise, the expertise determination module 214 determines theexpertise gap. In an embodiment, the expertise determination module 214utilizes the following equation to determine the expertise gap:

$\begin{matrix}{{\gamma( {b_{p},b_{r}} )} = \frac{{Dist}( {b_{p}^{\prime},b_{r}} )}{{Dist}( {b_{0},b_{*}} )}} & (1)\end{matrix}$where,

-   -   b_(p): Current expertise level;    -   b_(r): Required expertise level;    -   b_(p)′: if b_(p)(i)>b_(r)(i), then b_(p)′(i)=b_(r)(i) else        b_(p)′(i)=b_(p)(i);    -   i: domain;    -   Dist: Euclidean distance between the expertise levels;    -   b₀: No expertise    -   b_(*): Perfect expertise

At step 406, the second time interval required by each of the one ormore crowdworkers to complete each of the one or more tasks isdetermined. In an embodiment, the time interval determination module 216determines the second time interval. Prior to determining the secondtime interval, the time interval determination module 216 determines arate of learning for each of the one or more crowdworkers in each of theone or more skill sets based on the expertise gap. In an embodiment, thetime interval determination module 216 utilizes the following equationto determine the rate of learning:

$\begin{matrix}{{\alpha( {b_{p},b_{r}} )} = {\beta( {1 - \frac{\log( {1 + \frac{\gamma( {b_{p},b_{r}} )}{( {t( b_{r} )} )}} )}{\log( {v( b_{r} )} )}} )}} & (2)\end{matrix}$where,

-   -   α(b_(p),b_(r)): Rate of learning a particular skill set for a        crowdworker;    -   β: Universal rate of learning;    -   t(b_(r)): Time spent by a crowdworker on a task pertaining to a        particular skill set; and    -   v(b_(r)): Total number of tasks pertaining to the particular        skill set completed by the crowdworker.

After determining the rate of learning, the time interval determinationmodule 216 determines the second time interval based on the rate oflearning determined by using equation 2. In an embodiment, the timeinterval determination module 216 utilizes the following equation todetermine the second time interval:T _(n)′(b _(p) ,b _(r))=T ₁(b _(r))n ^(−α(b) ^(p) ^(,b) ^(r) ⁾  (3)where,

-   -   T_(n)′(b_(p),b_(r)): Expected time to complete n^(th) task        requiring b_(r) expertise; and    -   T₁(b_(r)): Ideal time required to complete a task requiring        b_(r) expertise.

At step 408, a check is performed whether the one or more crowdworkershave to be trained. In an embodiment, the task manager 218 performs thecheck. Prior to performing the check, the task manager 218 determinesthe skill set required by each of the one or more tasks from themetadata associated with the one or more tasks (stored as the task data232). Further, the task manager 218 categorizes each of the one or moretasks in the one or more expertise bins based on the required skill set.For example, the “task-1” is categorized in “skill-1” bin (refertable 1) as the “task-1” requires crowdworker possessing “skill-1”. Inan embodiment, each of one or more expertise bins includes a set oftasks. Thereafter, the task manager 218 determines the skill setspossessed by each of the one or more crowdworkers. In an embodiment, thetask manager 218 determines whether enough number of crowdworkerspossessing the required skill set is present to take up the set of taskspertaining to the required skill set. In an embodiment, the task manager218 further determines whether the enough number of crowdworkers havethe required level of expertise in the required skill set to take up theset of tasks. If the task manager 218 determines that the enough numberof crowdworkers is not present, the task manager 218 decides to trainthe one or more crowdworkers.

In an alternate embodiment, the task manager 218 extracts historicaldata from the historical task data 236. Based on the historical taskdata 236, the task manager 218 predicts the flow of reception of the oneor more tasks during the course of a day. Further, the task manager 218determines the availability of the one or more crowdworkers during thecourse of the day. Following table illustrates an example representationof the historical task data 236:

TABLE 3 Illustrates sample historical data Number of Number of taskscrowdworkers Time of the day Skill set Expertise level receivedavailable  9AM-12PM Skill-1 Basic 10 10 Advanced 5 5 Expert 0 0 Skill-2Basic 9 9 Advanced 2 2 Expert 2 2 12PM-3PM  Skill-1 Basic 2 2 Advanced 93 Expert 9 3 Skill-2 Basic 9 3 Advanced 10 5 Expert 2 2 3PM-6PM Skill-1Basic 5 5 Advanced 5 5 Expert 5 5 Skill-2 Basic 6 6 Advanced 6 6 Expert6 6

For example, between time interval “9 AM-12 PM” usually enoughcrowdworkers are available for each of the one or more skills set andthe expertise level. For instance, usually “10” tasks pertaining to the“skill-1” and requiring “basic” level of expertise are received betweenthe time interval “9 AM-12 PM”. Most of the time, 10 crowdworkers havingthe “basic” level of expertise in the “skill-1” are available.Similarly, between time interval “12 PM-3 PM” not enough number ofcrowdworkers are available that possess the “skill-2” in the “advanced”level of expertise. Thus, the task manager 218 determines that there isa need to train the crowdworkers that possess the “skill-2” in the“basic” level of expertise such that in predetermined period theyachieve the “advanced” level of expertise in the “skill-2”.

A person having ordinary skills in the art would understand that thescope of the disclosure is not limited to organizing the historical dataas illustrated in Table 3. In an embodiment, various other techniquessuch as, but are not limited to, a hierarchal model, a network model,and a relational model can be used for organizing the historical data.

The task manager 218 determines such time intervals where there is ashortage of crowdworkers possessing a particular skill set. In anembodiment, the task manager 218 starts training the crowdworkers forsuch time intervals. In an embodiment, the crowdworkers may be trainedto achieve higher level of expertise in the skills sets that thecrowdworkers possess. For example, the crowdworker having “basic” levelof expertise in the “skill-1” may be trained to achieve “advanced” levelof expertise in the “skill-1”. In another embodiment, the crowdworkersmay be trained on skills sets other than the skills set possessed by thecrowdworkers. For example, the crowdworker having the “advanced” levelof expertise in the “skill-1” may be trained to achieve the “basic”level of expertise in the “skill-2”.

If at step 408, it is determined that the one or more crowdworkers arenot to be trained, step 410 is performed. At step 410, the one or moretasks are assigned to the one or more crowdworkers based on theexpertise gap and the second time interval. In an embodiment, the taskmanager 218 assigns the one or more tasks. The task manager 218 assignsthe one or more tasks as per the skill set possessed by the one or morecrowdworkers. In an embodiment, the expertise gap of the one or morecrowdworkers in the respective skill set tends to zero (i.e., γ→0).Further, the second time interval for the crowdworker to complete a taskin the respective skill set approaches the ideal time interval (i.e.,T_(n)′→T₁). As the one or more tasks are assigned as per the skill setpossessed by the one or more crowdworkers, the number of instances wherethe SLA is violated is minimized. In an embodiment, each of the one ormore crowdworkers has a first set of tasks pertaining to the skill setspossessed by the one or more crowdworkers.

For example, a first crowdworker have an expertise gap of 0.5 in thefirst skill set and 0.1 expertise gap in second skill set. The taskmanager 218 has a first task pertaining to the first skill set and asecond task pertaining to the second skill set. The task manager 218would assign the second task to the first crowdworker as the expertisegap of the first crowdworker for the second skill set tends to zero. Asthe first crowdworker will be able to complete the second taskproficiently and accurately within the stipulated time, the chances ofSLA violations reduce.

If at step 408, it is determined that the one or more crowdworkers haveto be trained, step 412 is performed. At step 412, the at least onetraining task is assigned to the one or more crowdworkers. In anembodiment, the task manager 218 assigns the at least one training task.Additionally, the task manager 218 assigns the one or more tasks to theone or more crowdworkers in addition to the training task as describedin the step 410. In an embodiment, the training task may pertain toother skill sets that are not possessed by the one or more crowdworkers.In another embodiment, the training task may correspond to a task thatrequires a higher level expertise in a skill set than what is possessedby the one or more crowdworkers in the same skill set. In an embodiment,the training task is selected from the one or more tasks received by thecrowdsourcing platform server 102.

For example, a first crowdworker possesses the “basic” level ofexpertise in the “skill-1”. The crowdsourcing platform server 102receives the one or more tasks from the requestor such that the firstset of tasks from the one or more tasks requires the “basic” level ofexpertise in the “skill-1”. The remaining tasks from the one or moretasks require “advanced” level of expertise in the “skill-1”. Forinstance, not enough number of crowdworkers possessing “advanced” levelof expertise in the “skill-1” is available to take up the remainingtasks. In such a scenario, the task manager 218 decides to train thefirst crowdworker so that the first crowdworker attains “advanced” levelof expertise in the “skill-1”. The task manager 218 assigns the firstset of tasks to the first crowdworker. In addition to the first set oftasks, the task manager 218 assigns a task from the remaining tasks asthe training task to the first crowdworker.

In an embodiment, the number and frequency of the training tasks to beassigned to the one or more crowdworkers vary based on the time requiredby the crowdworker to complete the training task and the SLA associatedwith the one or more tasks. For example, a crowdworker does not have anyexperience in a first skill set and is an expert in a second skill set.Further, the crowdworker has an SLA to complete 15 tasks pertaining tothe second skill set in an hour. If the crowdworker consumes a lot oftime in completing the training task pertaining to the first skill set,the task manager 218 may reduce the frequency of assigning the trainingtask so that the SLA associated with the one or more tasks is notcompromised. For instance, initially if five training task were beingassigned to the crowdworker, the task manager 218 would reduce it to twotraining task per hour. In another embodiment, the task manager 218 mayassign the training task of lower difficulty so that the time taken bythe crowdworker to attempt this training task is reduced so that the SLAis not compromised.

In an embodiment, the number of training task assigned to the one ormore crowdworkers is determined based on the expertise gap of the one ormore crowdworkers in the one or more skill sets, and the predeterminedperiod in which the one or more crowdworkers is expected achieve therequired level of expertise. For example, the first crowdworker need towork on 10 training tasks to achieve the “advanced” level of expertise.Further, the predetermined period is one week. Thus, the task manager218 assigns at least two training tasks each day along with the firstset of tasks so that the first crowdworker achieves the “advanced” levelof expertise in a week's time.

At step 414, a response for the at least one training task is receivedfrom the one or more crowdworkers. In an embodiment, the communicationmanager 212 receives the response for the at least one training task.

At step 416, the response of the at least one training task isvalidated. In an embodiment, the validation module 220 validates theresponse of the at least one training task by comparing the responsewith the correct response. In an embodiment, the validation module 220stores the result of the validation as the validation data 238.

In alternate embodiment, the validation module 220 collects one or moreresponses for the at least one training task from the one or morecrowdworkers. Thereafter, the validation module 220 determines amajority of common responses from the one or more response. The majorityof common responses are treated as correct answers.

At step 418, an expertise gain for each of the one or more crowdworkersis determined based on the validation. In an embodiment, the expertisedetermination module 214 determines the expertise gain. The expertisedetermination module 214 extracts the validation result from thevalidation data 238. If the validation result is correct, the expertisedetermination module 214 updates the expertise gain. In an embodiment,the expertise determination module 214 utilizes following equation todetermine the expertise gain:b _(p) =b _(p)+α(b _(p) ,b _(r))log(1+γ(b _(p) ,b _(r)))*b _(p)  (4)

In an embodiment, bigger the expertise gap, higher an opportunity tolearn for the one or more crowdworker and hence greater is the gain oncompleting the training task. However, as the number of training taskscompleted by the one or more crowdworkers increases, the expertise gapdecreases as well as the expertise gain.

Additionally, the expertise determination module 214 determines thenumber of tasks that the one or more crowdworkers have to complete toachieve the required level of expertise in the particular skill setbased on the expertise gain and the expertise gap. In an embodiment, thetraining task is assigned to the one or more crowdworkers based on thenumber of training tasks.

In an embodiment, the learning curve predictor 224 predicts a learningcurve for each of the one or more crowdworkers based on the expertisegain and the rate of learning. An example of a learning curve isdescribed later in conjunction with FIG. 5A, and FIG. 5B.

At step 420, the current level of expertise of the one or morecrowdworkers is updated. In an embodiment, the expertise determinationmodule 214 updates the current level of expertise based on the expertisegain. In an embodiment, the expertise determination module 214 updatesthe current level of expertise post completion of the number of trainingtasks determined in the step 418.

As the training task is assigned to the one or more crowdworkers alongwith the first set of tasks, the one or more crowdworker get trainedwhile completing the first set of tasks. Further, as the one or morecrowdworker are proficient in the respective skills set, the SLAviolations associated with the first set of tasks is minimized.

FIG. 5A is a graphical representation 500 a of a learning curve of acrowdworker, in accordance with at least one embodiment.

The graphical representation 500 a includes a graph 502. The X-axis forthe graph 502 represents “number of training tasks” (depicted by 506).The Y-axis of the graph 502 represents the “time taken to complete atraining task” (depicted by 504). As it can be observed from the graph502 that the time taken by the crowdworker to compete the training taskreduces with the number of training tasks completed by the crowdworker.

FIG. 5B is a graphical representation 500 b of a learning curve of acrowdworker, in accordance with at least one embodiment.

The graphical representation 500 b includes a graph 508. The X-axis forthe graph 508 represents “number of training tasks” (depicted by 506).The Y-axis of the graph 504 represents “learning rate” (depicted by510). As from the graph 508, it can be observed that the learning ratereaches saturation with the number of training tasks completed by thecrowdworker.

The disclosed embodiments encompass numerous advantages. The one or morecrowdworkers are presented with one or more tasks as per the respectiveskills set. Along with the one or more tasks, the one or morecrowdworkers are assigned with a training task. The training task maycorrespond to task that require other skill or an upgrade in the presentskill that the crowdworker has. As the one or more crowdworker areproficient in the respective skills set, the SLA violations associatedwith the first set of tasks is minimized. Other than the first set oftasks, the one or more crowdworkers work on the training task. Thus, theone or more crowdworkers get trained on the other skill set along withcompleting the first set of tasks pertaining to respective skill sets.Therefore, the one or more crowdworker are trained on the other skillset or able to upgrade their skills without violating the SLA of thefirst set of tasks assigned as per the respective skill sets.

The disclosed methods and systems, as illustrated in the ongoingdescription or any of its components, may be embodied in the form of acomputer system. Typical examples of a computer system include ageneral-purpose computer, a programmed microprocessor, amicro-controller, a peripheral integrated circuit element, and otherdevices, or arrangements of devices that are capable of implementing thesteps that constitute the method of the disclosure.

The computer system comprises a computer, an input device, a displayunit and the Internet. The computer further comprises a microprocessor.The microprocessor is connected to a communication bus. The computeralso includes a memory. The memory may be Random Access Memory (RAM) orRead Only Memory (ROM). The computer system further comprises a storagedevice, which may be a hard-disk drive or a removable storage drive,such as, a floppy-disk drive, optical-disk drive, and the like. Thestorage device may also be a means for loading computer programs orother instructions into the computer system. The computer system alsoincludes a communication unit. The communication unit allows thecomputer to connect to other databases and the Internet through aninput/output (I/O) interface, allowing the transfer as well as receptionof data from other sources. The communication unit may include a modem,an Ethernet card, or other similar devices, which enable the computersystem to connect to databases and networks, such as, LAN, MAN, WAN, andthe Internet. The computer system facilitates input from a user throughinput devices accessible to the system through an I/O interface.

In order to process input data, the computer system executes a set ofinstructions that are stored in one or more storage elements. Thestorage elements may also hold data or other information, as desired.The storage element may be in the form of an information source or aphysical memory element present in the processing machine.

The programmable or computer-readable instructions may include variouscommands that instruct the processing machine to perform specific tasks,such as steps that constitute the method of the disclosure. The systemsand methods described can also be implemented using only softwareprogramming or using only hardware or by a varying combination of thetwo techniques. The disclosure is independent of the programminglanguage and the operating system used in the computers. Theinstructions for the disclosure can be written in all programminglanguages including, but not limited to, ‘C’, ‘C++’, ‘Visual C++’ and‘Visual Basic’. Further, the software may be in the form of a collectionof separate programs, a program module containing a larger program or aportion of a program module, as discussed in the ongoing description.The software may also include modular programming in the form ofobject-oriented programming. The processing of input data by theprocessing machine may be in response to user commands, the results ofprevious processing, or from a request made by another processingmachine. The disclosure can also be implemented in all operating systemsand platforms including, but not limited to, ‘Unix’, DOS′, ‘Android’,‘Symbian’, and ‘Linux’.

The programmable instructions can be stored and transmitted on acomputer-readable medium. The disclosure can also be embodied in acomputer program product comprising a computer-readable medium, or withany product capable of implementing the above methods and systems, orthe numerous possible variations thereof.

Various embodiments of the methods and systems for training acrowdworker have been disclosed. However, it should be apparent to thoseskilled in the art that modifications in addition to those described,are possible without departing from the inventive concepts herein. Theembodiments, therefore, are not restrictive, except in the spirit of thedisclosure. Moreover, in interpreting the disclosure, all terms shouldbe understood in the broadest possible manner consistent with thecontext. In particular, the terms “comprises” and “comprising” should beinterpreted as referring to elements, components, or steps, in anon-exclusive manner, indicating that the referenced elements,components, or steps may be present, or utilized, or combined with otherelements, components, or steps that are not expressly referenced.

A person having ordinary skills in the art will appreciate that thesystem, modules, and sub-modules have been illustrated and explained toserve as examples and should not be considered limiting in any manner.It will be further appreciated that the variants of the above disclosedsystem elements, or modules and other features and functions, oralternatives thereof, may be combined to create other different systemsor applications.

Those skilled in the art will appreciate that any of the aforementionedsteps and/or system modules may be suitably replaced, reordered, orremoved, and additional steps and/or system modules may be inserted,depending on the needs of a particular application. In addition, thesystems of the aforementioned embodiments may be implemented using awide variety of suitable processes and system modules and is not limitedto any particular computer hardware, software, middleware, firmware,microcode, or the like.

The claims can encompass embodiments for hardware, software, or acombination thereof.

It will be appreciated that variants of the above disclosed, and otherfeatures and functions or alternatives thereof, may be combined intomany other different systems or applications. Presently unforeseen orunanticipated alternatives, modifications, variations, or improvementstherein may be subsequently made by those skilled in the art, which arealso intended to be encompassed by the following claims.

What is claimed is:
 1. A method for achieving efficient task processingusing automation techniques based on predicted conditions, the methodcomprising: predicting, by a processor, a flow of received tasks and anavailability of crowdworkers of a first level of expertise and secondlevel of expertise over a predetermined period of time, the predictingbeing based on historical data, wherein the flow of received tasksincludes a first set of tasks corresponding to the first level ofexpertise and a second set of tasks corresponding to a second level ofexpertise; determining, by the processor, based on the predicted flow ofreceived tasks and the predicted availability of crowdworkers, to traina first number of crowdworkers such that the predicted availability ofcrowdworkers of the second level of expertise and the trained firstnumber of crowdworkers satisfies a demand based on the predicted flow ofthe second set of tasks over the predetermined period of time;determining, by the processor, an expertise gap between a current levelof expertise possessed by a first crowdworker and the second level ofexpertise, wherein the current level of expertise meets or exceeds thefirst level of expertise and wherein determining the expertise gapcomprises: converting the current level of expertise of the firstcrowdworker, the second level of expertise, and the first level ofexpertise to discrete values based on an expertise mapping table, andcalculating a ratio of a Euclidean distance between the discrete valueof the current level of expertise and the discrete value of the discretevalue of the second level of expertise and a Euclidean distance betweena discrete value of a lowest theoretical expertise and a discrete valueof a highest theoretical expertise, wherein the ratio represents theexpertise gap; calculating, by the processor, a learning rate for thefirst crowdworker, wherein the learning rate is calculated based on auniversal rate of learning that is modified by the expertise gap;identifying, by the processor, training tasks for the first crowdworkerto achieve the second level of expertise based on the determinedexpertise gap; determining, by the processor, a predicted set of tasksthat comprise non-training tasks predicted to be received prior to thepredetermined period of time; accessing, by the processor, servicerequirements for the first set of tasks; and training, by the processor,the crowdworkers such that upon completion of the training of thecrowdworkers, the predicted availability of crowdworkers of the secondlevel of expertise will satisfy the demand based on the predicted flowof the second set of tasks, wherein training the crowdworkers comprises:assigning, based on the learning rate, a set of training tasks to thefirst crowdworker such that the first crowdworker comprises availabilityfor the non-training tasks of the predicted set of tasks and such thatprocessing requirements for the predicted set of tasks are met by thefirst crowdworker and other crowdworkers that are available to processthe predicted set of tasks and that comprise a skill level required toprocess the predicted set of tasks.
 2. The method of claim 1, furthercomprising determining, by the processor, a first time intervalcorresponding to the first crowdworker for completing the identifiedtraining tasks based on the expertise gap.
 3. The method of claim 2,further comprising validating, by the processor, a response to at leastone of the identified training tasks from the first crowdworker.
 4. Themethod of claim 3, wherein the learning rate of the first crowdworker isbased on the first time interval, the validation, and the expertise gap,and wherein the learning rate is indicative of a level of expertisegained by the first crowdworker by completing the at least oneidentified training tasks.
 5. The method of claim 4, further comprisingdetermining, by the processor, a number of training tasks for the firstcrowdworker to achieve the second level of expertise based on the firsttime interval the expertise gap.
 6. The method of claim 4, furthercomprising determining, by the processor, an expertise gain based on theexpertise gap, the learning rate, the validation, and the first timeinterval, wherein the expertise gain is indicative of a gain inexpertise by the first crowdworker by completing the training tasks. 7.The method of claim 4, further comprising updating, by the processor,the level of expertise of the first crowdworker based on the validation,the learning rate, and the first time interval.
 8. The method of claim1, wherein the predetermined period of time comprises a time of day on agiven day of a week.
 9. A crowdsourcing platform server for achievingefficient task processing using automation techniques based on predictedconditions, the crowdsourcing platform server comprising: a processorand memory configured to: predict a flow of received tasks and anavailability of crowdworkers of a first level of expertise and secondlevel of expertise over a predetermined period of time, the predictingbeing based on historical data, wherein the flow of received tasksincludes a first set of tasks corresponding to the first level ofexpertise and a second set of tasks corresponding to a second level ofexpertise; determine based on the predicted flow of received tasks andthe predicted availability of crowdworkers, to train a first numbercrowdworkers such that the predicted availability of crowdworkers of thesecond level of expertise and the trained first number of crowdworkerssatisfies a demand based on the predicted flow of the second set oftasks over a predetermined period of time; determine an expertise gapbetween a current level of expertise possessed by a first crowdworkerand the second level of expertise, wherein the current level ofexpertise meets or exceeds the first level of expertise and whereindetermining the expertise gap comprises: converting the current level ofexpertise of the first crowdworker, the second level of expertise, andthe first level of expertise to discrete values based on an expertisemapping table, and calculating a ratio of a Euclidean distance betweenthe discrete value of the current level of expertise and the discretevalue of the discrete value of the second level of expertise and aEuclidean distance between a discrete value of a lowest theoreticalexpertise and a discrete value of a highest theoretical expertise,wherein the ratio represents the expertise gap; calculate a learningrate for the first crowdworker, wherein the learning rate is calculatedbased on a universal rate of learning that is modified by the expertisegap; identify training tasks for the first crowdworker to achieve thesecond level of expertise based on the determined expertise gap;determine a predicted set of tasks that comprise non-training taskspredicted to be received prior to the predetermined period of time;access service requirements for the first set of tasks; and train thecrowdworkers such that upon completion of the training of thecrowdworkers, the predicted availability of crowdworkers of the secondlevel of expertise will satisfy the demand based on the predicted flowof the second set of tasks, wherein training the crowdworkers comprises:assigning, based on the learning rate, a set of training tasks to thefirst crowdworker such that the first crowdworker comprises availabilityfor the non-training tasks of the predicted set of tasks and such thatprocessing requirements for the predicted set of tasks are met by thefirst crowdworker and other crowdworkers that are available to processthe predicted set of tasks and that comprise a skill level required toprocess the predicted set of tasks.
 10. The server of claim 9, whereinthe processor and memory are further configured to: determine a firsttime interval corresponding to the first crowdworker for completing theidentified training tasks based on the expertise gap.
 11. The server ofclaim 10, wherein the processor and memory are further configured to:validate a response to at least one of the identified training tasksfrom the first crowdworker.
 12. The server of claim 11, wherein thelearning rate of the first crowdworker is based on the first timeinterval, the validation, and the expertise gap, and wherein thelearning rate is indicative of a level of expertise gained by the firstcrowdworker by completing the at least one identified training tasks.13. The server of claim 12, wherein the processor and memory are furtherconfigured to: determine a number of training tasks for the firstcrowdworker to achieve the second level of expertise based on the firsttime interval the expertise gap.
 14. A computer program product forachieving efficient task processing using automation techniques based onpredicted conditions, the computer program product comprising anon-transitory computer readable medium embodied therein comprising aset of instructions executable by a processor for training acrowdworker, the processor executing the set of instructions to: predicta flow of received tasks and an availability of crowdworkers of a firstlevel of expertise and second level of expertise over a predeterminedperiod of time, the predicting being based on historical data, whereinthe flow of received tasks includes a first set of tasks correspondingto the first level of expertise and a second set of tasks correspondingto a second level of expertise; determine based on the predicted flow ofreceived tasks and the predicted availability of crowd workers, to traina first number crowdworkers such that the predicted availability ofcrowdworkers of the second level of expertise and the trained firstnumber of crowdworkers satisfies a demand based on the predicted flow ofthe second set of tasks over a predetermined period of time; determinean expertise gap between a current level of expertise possessed by afirst crowdworker and the second level of expertise, wherein the currentlevel of expertise meets or exceeds the first level of expertise andwherein determining the expertise gap comprises: converting the currentlevel of expertise of the first crowdworker, the second level ofexpertise, and the first level of expertise to discrete values based onan expertise mapping table, and calculating a ratio of a Euclideandistance between the discrete value of the current level of expertiseand the discrete value of the discrete value of the second level ofexpertise and a Euclidean distance between a discrete value of a lowesttheoretical expertise and a discrete value of a highest theoreticalexpertise, wherein the ratio represents the expertise gap; calculate alearning rate for the first crowdworker, wherein the learning rate iscalculated based on a universal rate of learning that is modified by theexpertise gap; identify training tasks for the first crowdworker toachieve the second level of expertise based on the determined expertisegap; determining a predicted set of tasks that comprise non-trainingtasks predicted to be received prior to the predetermined period oftime; accessing service requirements for the first set of tasks; andtrain the crowdworkers such that upon completion of the training of thecrowdworkers, the predicted availability of crowdworkers of the secondlevel of expertise will satisfy the demand based on the predicted flowof the second set of tasks, wherein training the crowdworkers comprises:assigning, based on the learning rate, a set of training tasks to thefirst crowdworker such that the first crowdworker comprises availabilityfor the non-training tasks of the predicted set of tasks and such thatprocessing requirements for the predicted set of tasks are met by thefirst crowdworker and other crowdworkers that are available to processthe predicted set of tasks and that comprise a skill level required toprocess the predicted set of tasks.
 15. The product of claim 14, whereinthe processor further executes the set of instructions to determine afirst time interval corresponding to the first crowdworker forcompleting the identified training tasks based on the expertise gap. 16.The product of claim 15, wherein the processor further executes the setof instructions to validate a response to at least one of the identifiedtraining tasks from the first crowdworker.
 17. The product of claim 16,wherein the learning rate of the first crowdworker is based on the firsttime interval, the validation, and the expertise gap, and wherein thelearning rate is indicative of a level of expertise gained by the firstcrowdworker by completing the at least one identified training tasks.